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Glama

Duffel

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate idempotentHint=true (safe to repeat) and non-destructive, so the description adds value by explaining scoping by identifier, persistence differences (authenticated vs anonymous), and pairing with recall/forget. No contradiction; behavior is well-covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences: purpose, guidance, and mechanics. Every sentence adds new, non-redundant information. No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter write tool with no output schema, the description covers purpose, usage, persistence behavior, and sibling relationships comprehensively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for key and value. The tool description adds extra context like scoping by identifier and use-case examples, raising it above the baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool saves data for reuse across conversations/sessions, with specific examples (resolved ticker, target address, user preference, research subject). It distinguishes from sibling tools recall and forget by framing them as companion actions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use ('when you discover something worth carrying forward') and pairs with recall/forget for retrieval and deletion. It lacks an explicit 'do not use when' clause but strong contextual signals make selection straightforward.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.5/5.0
Disambiguation2/5

Several tools are near-duplicates or have blurry boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical today, and the six polymarket tools (edges, arbitrage, bet_research, fill_risk, edge_tracker, kalshi_spread) all target opportunity-finding in overlapping ways. Detailed descriptions help, but an agent can easily pick the wrong one.

Naming Consistency3/5

Most names are readable snake_case with clear verbs like ask_, compare_, resolve_, and validate_, but conventions are mixed: noun-style names (entity_profile, recent_alerts, polymarket_edges) sit beside imperative verbs and domain-prefixed families. There is no camelCase or total chaos, so it is inconsistent but navigable.

Tool Count2/5

32 tools is heavy and exceeds the well-scoped zone; the set spans flight search, general data Q&A, prediction markets, memory, subscriptions, and feedback. Many are platform meta-tools that could be consolidated or hidden behind the main ask_pipeworx router.

Completeness2/5

If Duffel is the intended domain, only duffel_flight_search is present—there is no offer detail, booking, order management, or cancellation, so travel workflows dead-end. As a generic Pipeworx data platform the surface is broader, but that only highlights the mismatch with the server name and a missing coherent domain.

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